Machine Learning Regression (MLR) diagnostics#
This module provides various tools to create and evaluate MLR models for arbitrary input variables.
Examples#
Constraining uncertainty in projected gross primary production (GPP) with machine learning: Use Gradient Boosted Regression Tree (GBRT) algorithm to constrain projected Gross Primary Production (GPP) in RCP 8.5 scenario using observations of process-based predictors.
Diagnostic scripts#
Auxiliary scripts#
Available MLR models#
- Gradient Boosted Regression Trees (sklearn implementation)
- Gradient Boosted Regression Trees (xgboost implementation)
- Gaussian Process Regression (sklearn implementation)
- Huber Regression
- Kernel Ridge Regression
- LASSO Regression
- LASSO Regression with built-in CV
- LASSO Regression (using Least-angle Regression algorithm) with built-in CV
- Linear Regression
- Random Forest Regression
- Ridge Regression
- Ridge Regression with built-in CV
- Support Vector Regression